System, method and recording medium for calculating physiological index

ABSTRACT

A system, a method and a recording medium for calculating a physiological index are provided. The method includes: dividing a physiological data sequence into a plurality of windows; analyzing a data segment in each window to obtain metadata that represents data characteristics of the data segment; updating the metadata including the data characteristics of all data segments in the windows up to a previous window by using the metadata corresponding to one of the windows to obtain the metadata including the data characteristics of all data segments in the windows up to a current window; and finally, calculating the physiological index by using the updated metadata.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefits of U.S. provisionalapplication Ser. No. 61/497,965, filed on Jun. 17, 2011 and Taiwanapplication serial no. 100146537, filed on Dec. 15, 2011. The entiretyof each of the above-mentioned patent applications is herebyincorporated by reference herein and made a part of this specification.

BACKGROUND

1. Technical Field

The disclosure relates to a system, a method and a recording medium forcalculating a physiological index.

2. Related Art

Using information technology to collect various physiological signalsand analyze physiological heath status of an individual case is a jointcollaborative research topic for the fields of medical care andinformation science. For example, analysis of an electrocardiogram (ECG)signal has been a very important issue in analysis ofcardiovascular-related diseases, because it can directly reflect thestatus of heart function.

Most signs of diseases will show slight differences in the variabilityof operation and rhythm of physical organs, although many internationalcompanies and medical researchers have provided processes and methodsfor monitoring and analyzing the physiological signal, there are stillsome technical problems to be solved.

Taking the ECG as an example, current heart function examination mainlyuses short-term ECG analysis. As many diseases cannot be detected fromshort-term ECG, researchers have developed physiological indexes thatare mainly obtained by analyzing the complexity of the heart rhythm fromthe multi-scale perspective using long-term ECG in recent years. It isverified in researches that this type of indexes can exactly reflect thehealth status of the heart function. Calculation of multi-scalephysiological index is more complex than the conventional statisticalanalysis of time-frequency domain, especially the effectiveness ofmulti-scale entropy (MSE) based on entropy has been proven in medicalresearches.

Although the long-term ECG analysis can provide complete physiologicalinformation of an individual case, the system needs a large space forstoring long-term ECG data. How to design a new mechanism that canefficiently store the ECG information while calculating a long-term ECGphysiological index is one of the challenges in long-term ECG analysis.

The long-term physiological index that is developed based on multiplescales can present a physiological state of an individual case in along-term range, but the difference of the physiological state cannot beobtained through analysis of a short-term physiological signal. However,due to a considerable computation time, the application of long-termphysiological index is restricted in interpretation of and research ofsymptoms after an individual case is attacked, and is not applied inmonitoring and early warning of a physiological state of an individualcase. It can be seen that, how to enable this type of multi-scalephysiological indexes to have the capability of monitoring andevaluating the physiological state of an individual case in real time asfar as possible is a very important issue in physiological monitoring ofan individual case in clinic.

SUMMARY

The disclosure is directed to a system, a method and a recording mediumfor calculating a physiological index.

A method for calculating a physiological index is introduced herein,which is applicable in an electronic device. The method includes:dividing a physiological data sequence into a plurality of windows, inwhich each window includes a data segment of the physiological datasequence; analyzing the data segment in each window to obtain metadatathat represents data characteristics of the data segment; updatingmetadata including the data characteristics of all data segments in thewindows up to a previous window by using the metadata corresponding toone of the windows to obtain the metadata including the datacharacteristics of all data segments in the windows up to a currentwindow; and calculating a physiological index by using the updatedmetadata.

A system for calculating a physiological index is introduced herein,which includes a converter and a computer system. The converter is usedfor detecting a physiological data sequence. The computer systemincludes a transmission interface, at least one storage medium, and aprocessor. The transmission interface is connected to the converter andis used for receiving the physiological data sequence. The at least onestorage medium is used for storing the physiological data sequence. Theprocessor is coupled to the transmission interface and the at least onestorage medium, and is used for dividing the physiological data sequenceinto a plurality of windows, and analyzing a data segment of thephysiological data sequence in each window to obtain metadata thatrepresents data characteristics of the data segment, updating metadataincluding the data characteristics of all data segments in the windowsup to a previous window by using the metadata corresponding to one ofthe windows to obtain the metadata including the data characteristics ofall data segments in the windows up to a current window; and calculatinga physiological index by using the updated metadata.

A computer readable recording medium with a stored program is introducedherein, which can complete the method when the program is loaded on acomputer and is executed.

Several exemplary embodiments accompanied with figures are described indetail below to further describe the disclosure in details.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide further understanding,and are incorporated in and constitute a part of this specification. Thedrawings illustrate exemplary embodiments and, together with thedescription, serve to explain the principles of the disclosure.

FIG. 1 is a flowchart of a method for calculating a physiological indexaccording to an embodiment of the disclosure.

FIG. 2 is a diagram of a method for calculating a physiological indexaccording to an embodiment of the disclosure.

FIG. 3( a) and FIG. 3( b) show examples of a coarse-graining procedureaccording to an embodiment of the disclosure.

FIG. 4 is a histogram corresponding to a data structure for calculatingMSE according to an embodiment of the disclosure.

FIG. 5 shows an example of storing and updating metadata by using asparse matrix according to an embodiment of the disclosure.

FIG. 6 is a schematic view of calculating MSE by using continuouslyupdated metadata according to an embodiment of the disclosure.

FIG. 7( a), FIG. 7( b), and FIG. 7( c) show examples of recordingmetadata by using a tree data structure and calculating a physiologicalindex according to the metadata according to an embodiment of thedisclosure.

FIG. 8 is a function block diagram of a system for calculating aphysiological index for executing the methods in FIG. 1 to FIG. 7according to some embodiments.

FIG. 9 is a time analysis chart of calculating MSE when a scale is setto be 1 by using four evaluation methods according to an embodiment ofthe disclosure.

FIG. 10 shows comparison of calculating MSE by using a structured methodaccording to an embodiment of the disclosure.

FIG. 11 is an analysis chart of total time consumed in calculating MSEwhen a scale is set to be 1 to 20 by using four evaluation methodsaccording to an embodiment of the disclosure.

FIG. 12 shows analysis of time for calculating MSE by using a datastructured method in FIG. 11.

FIG. 13( a) and FIG. 13( b) show examples of recording metadata by usingdata distribution statistics and calculating a physiological indexaccording to the metadata according to an embodiment of the disclosure.

FIG. 14( a) and FIG. 14( b) are total time analysis charts ofcalculating MSE when a scale is set to be 1 to 20 by using fiveevaluation methods according to an embodiment of the disclosure.

DETAILED DESCRIPTION OF DISCLOSED EMBODIMENTS

The disclosure provides a method for calculating a physiological indexin long-term physiological data analysis, in which physiological datathat gradually enters a system is divided into relative shortphysiological data segments by using the concept of the window, so as tosolve the problem of low efficiency caused by a large amount of data inbatch mode calculation. Additionally, as for the problem of storagespace for long-term physiological data, an embodiment of the disclosurealso provides a method for replacing originally stored physiologicaldata with metadata characteristics, in which, when entering each window,information of metadata is updated to describe characteristics of allprevious data. Finally, an embodiment of the disclosure provides amethod for calculating a long-term physiological index throughcombination of systematic data structure and data of the metadata.Through the three processes mentioned above, the disclosure enables thesystem to provide a long-term physiological index, especially themulti-scale entropy (MSE) index that is the most complex in calculationin multi-scale analysis. An embodiment of the disclosure also providesdata for health care workers, so that monitoring and analysis of along-term physiological state can be widely adopted in clinicalpractice.

In the following embodiments, an ECG is described as an example, but theapplication of the disclosure is not limited to the ECG.

FIG. 1 is a flowchart of a method for calculating a physiological indexaccording to an embodiment of the disclosure. Referring to FIG. 1, thecalculation of a physiological index of this embodiment is applicable tovarious electronic devices with the calculation capability, and mainlyincludes division of a data sequence, calculation of metadata,accumulation and storage of metadata, and calculation of a physiologicalindex, which are described as follows.

In Step S102, an electronic device receives a physiological datasequence that gradually enters the device, and divides the sequence intoa plurality of windows, in which each window includes a data segment ofthe physiological data sequence. In particular, the division of the datasequence according to this embodiment includes, for example, sequencesthat represent each heartbeat cycle information such as R-R interval(RRI) and P-R interval (PRI) in an ECG signal are used to define a sizeof a window according to a fixed duration (for example, a half hour oran hour) or data length (for example, 5,000 RRI data points or 10,000RRI data points). An original long-term data sequence (for example, a24-hour RRI sequence) is divided into a plurality of non-overlappingdata segments, so that the subsequent processing is to performcalculation on each data segment.

It should be noted that, the physiological data sequence is described bytaking a data sequence of features of an ECG as example, and thefeatures of the ECG include an R-R interval of adjacent heartbeats, aP-R interval in a single heartbeat, a QRS duration, an ST segmentduration in an ECG measured from a temporal perspective, a delta of a Pwave, an R wave, an S wave, and a T wave potential change betweenadjacent heartbeats measured from a spatial perspective, and a delta ora similarity of a pattern difference between adjacent ECGs measured froma morphological perspective. In addition to the data sequence of thefeatures of an ECG record, the method of this embodiment is also usedfor other physiological data sequences, for example, features of a datasequence of an electroencephalogram record, a record of breathingsignals or several kinds of oxygen saturation signals may also adopt themethod of this embodiment to calculate a corresponding physiologicalindex.

In Step S104, the electronic device analyzes the data segment in eachwindow to obtain metadata that represents data characteristics of thedata segment. In particular, according to the calculation property ofthe physiological index to be calculated, this embodiment can analyzemetadata that is required for calculating the physiological index andthe calculation manner, in which the metadata can be used to calculatethe physiological index.

It should be noted that, the metadata is used to, for example, representstatistical descriptions of the data characteristics, data structurecharacteristics, trend information, or a data randomness measurementvalue. The statistical description includes a mean value, a standarddeviation, a mode, a median, a coefficient of skewness, a coefficient ofkurtosis, or parameters of probability distribution. The data structurecharacteristics include grouping or counting values of data histogram.The trend information includes a regression coefficient or a polynomialcoefficient. The data randomness includes entropy or a temporalasymmetric index, which is not limited herein.

In Step S106, the electronic device updates metadata including the datacharacteristics of all data segments in the windows up to a previouswindow by using the metadata corresponding to one of the windows toobtain the metadata including the data characteristics of all datasegments in the windows up to a current window. In particular, with thegradual entrance of data sequence into the system, this embodimentprovides a metadata update method, so that the updated metadata canrepresent overall characteristics of the data sequences that haveentered the system. It should be noted that, as for storage of themetadata, this embodiment particularly uses a multi-dimensional sparsematrix or tree data structure to record the metadata, and the specificimplementation manner is described in detail in the followingembodiments.

In Step S108, the electronic device calculates a physiological index byusing the updated metadata. In particular, after the metadata of eachtime segment is updated, the updated metadata can be used to calculatethe physiological index. As the metadata is different from the originalphysiological data sequence, the method for calculating thephysiological index is also different from the conventional method. Inorder to calculate the physiological index, this embodiment providesadditional data processing architecture for calculation, and thespecific implementation manner is described in detail in the followingembodiments.

FIG. 2 is a diagram of a method for calculating a physiological indexaccording to an embodiment of the disclosure. Referring to FIG. 2, inthis embodiment, a physiological data sequence 20 is divided into aplurality of short-duration data segments (including an RRI data segment1, an RRI data segment 2, . . . , an RRI data segment k) according to afixed duration (T₁, T₂, . . . , T_(k)). During analysis of the datasegments, in this embodiment, a coarse-graining procedure in multi-scaleanalysis is first executed, and then metadata (including metadata 1,metadata 2, . . . , metadata k) that represents data characteristics ofeach data segment is calculated and stored by using a specific datastructure. The metadata is gradually updated with each entered datasegment to obtain the metadata that represents all long-term ECGcharacteristics, and finally, calculation of approximate entropy orsample entropy is performed, so as to obtain a desired long-termphysiological index 22 and complete the calculation procedure of MSE.

It should be noted that, the coarse-graining procedure includes, forexample, calculating the data segment in each window by using aplurality of scales respectively to obtain a data sequence under eachscale, and using the data sequence to calculate metadata that representsdata characteristics of the data segment. When executing thecoarse-graining procedure on the data segment by using one of thescales, for example, with the used scale as a cell, a plurality ofbatches of data in the data segment is selected in sequence, and anaverage of the selected data is calculated and is used as a batch ofdata in the data sequence under the scale.

For example, FIG. 3( a) and FIG. 3( b) show examples of acoarse-graining procedure according to an embodiment of the disclosure.Referring to FIG. 3( a) and FIG. 3( b) together, as for a specified datasequence X={x_(i)}, in which 1≦i≦N, a data sequence v_(j) ^((τ)) afterperforming the coarse-graining procedure on the data sequence X can beobtained through the following formula:

${v_{j}^{(\tau)} = {\frac{1}{\tau}{\sum\limits_{i = {{{({j - 1})} \times \tau} + 1}}^{j \times \tau}\; x_{i}}}},{1 \leq j \leq {\frac{N}{\tau}.}}$

In the formula, N is a total number of batches of data included in thedata sequence X, τ is a selected coarse-graining scale. It can be knownfrom FIG. 3( a) that, when scale τ=2, that is, 2 batches of data in thedata segment Xis selected in sequence with 2 as a cell, for example,(x₁, x₂), (x₃, x₄), (x₅, x₆), . . . , x_(i), x_(i+1)) . . . , and anaverage of the selected data is calculated and used as the data in thedata sequence under the scale, so as to finally obtain a data sequenceV_(τ)=V₂=(v₁, v₂, v₃, . . . ) after the coarse-graining procedure.Similarly, it can be known form FIG. 3( b) that, when scale τ=3, 3batches of data in the data segment Xis selected in sequence with 3 as acell, and an average of the selected data is calculated and is used asthe data in the data sequence under the scale, so as to finally obtain adata sequence V3=(v₁′, v₂′, v₃′, . . . ) after the coarse-grainingprocedure. Taking an actual number as an example, for an original datasequence X=(26, 28, 30, 26, 26, 27, 25), after the coarse-grainingprocedure (τ=2) is performed, a data sequence V₂=(27, 28, 26.5) isobtained.

As for the calculation and updating of the metadata, FIG. 4 is ahistogram corresponding to a data structure for calculating MSEaccording to an embodiment of the disclosure. Referring to FIG. 4,histogram 40 is corresponding to a data structure that is used forcalculating metadata deduced when the dimension of an observed sample isset to be m=2 in MSE according to this embodiment. As calculation of theapproximate entropy or the sample entropy needs to compute statisticsthat can represent the relationship between each sample point and othersample points (for example, delta of a sample value), in thisembodiment, multi-dimensional histogram process architecture is used toarrange the sample points (two-dimensional vector in this embodiment)according to the scale sample value, and the number of occurrences ofeach combination (two-dimensional vector) is calculated, therebyorganized into a two-dimensional statistical table. When the dimensionof observed sample is set to be m=3, the processing manner may beorganized into a three-dimensional statistical table.

Although the number of sample points of the data segment entered eachtime may be up to several thousands/ten thousands, under the limitationof conditions of a first dimension sample value, the distributive scopeof a second dimension sample value is extremely limited, this phenomenonis very reasonable for physical analysis of cardiac cycle, because thedifference between two adjacent heartbeat cycle is not large. Thephenomenon is more obvious in limiting a third dimension distribution(set to be m=3) under the first dimension and the second dimensionsample value (that is, variation of the third dimension sample value isalso limited).

According to the observed phenomenon, in a two-dimensional statisticaltable or a three-dimensional statistical table, the probability thateach cell is valued is much lower than the probability that each cell isnon-valued (that is, combination of the two dimensions or the threedimensions does not appear in the data). If it is intended to completelyrecord the two-dimensional statistical table or the three-dimensionalstatistical table, it needs a lot of storage. Accordingly, amulti-dimensional sparse matrix may be used to record the statisticaltable.

In particular, as for the data sequence under each scale in each windowdivided from the physiological data sequence, in the embodiment of thedisclosure, metadata corresponding to one window is recorded in amulti-dimensional sparse matrix, and then metadata corresponding toother windows is accumulated in sequence to the same multi-dimensionalsparse matrix, so that the multi-dimensional sparse matrix includesmetadata including data characteristics of all data segments up to acurrent window.

The step of recording the metadata includes, for example, first,recording a count of each vector combination included by the metadatacorresponding to one window in a multi-dimensional sparse matrix, andthen accumulating a count of each vector combination included by themetadata corresponding to the other windows in sequence to the count ofthe vector combination that is recorded in the multi-dimensional sparsematrix. The vector combination that is accumulated to themulti-dimensional sparse matrix may have different or the same partswith the original vector combination in the multi-dimensional sparsematrix. As for the same parts of the second vector combination and thefirst vector combination, the counts of the two combinations areaccumulated; on the contrary, as for the different parts of the secondvector combination and the first vector combination, no correspondingfirst vector combination exists in the multi-dimensional sparse matrix,so in the embodiment of the disclosure, as for the relative positions ofall vector combinations in the multi-dimensional sparse matrix, the sizeof the multi-dimensional sparse matrix needs to be moderately expandedaccording to the second vector combination, so as to bring the secondvector combination into the multi-dimensional sparse matrix and use thesecond vector combination as a newly added vector combination.

For example, FIG. 5 shows an example of storing and updating metadata byusing a sparse matrix according to an embodiment of the disclosure.Referring to FIG. 5, this embodiment describes the recording andupdating manner of metadata under the conditions that the scale forcalculating the MSE is set to be m=2. It can be known from FIG. 5, inthis embodiment, metadata (including information of previous t timesegments) of a t^(th) time segment (corresponding to a window t)recorded and updated in the multi-dimensional sparse matrix and metadatacalculated by a (t+1)^(th) time segment (corresponding to a window t+1)are converted into a full information matrix for metadata update, andthe metadata in the information matrix after updating is recorded in aform of a multi-dimensional sparse matrix to serve as metadata updatedto the (t+1)^(th) time segment. In this way, the information matrixconverted from the metadata continuously updates the informationaccording to each data segment entered in the order of time, and thesize of the information matrix may be expanded or remained unchanged ineach updating process.

FIG. 6 is a schematic view of calculating MSE by using continuouslyupdated metadata according to an embodiment of the disclosure. Referringto FIG. 6, as statistics on the number of occurrences of each vectorcombination is continuously performed when the metadata is updated, inthis embodiment, an information matrix 60 converted from the metadatasets that an upper bound of delta values defined in the MSE asr=0.15×SD, in which SD is a standard deviation of all RRI data up to acurrent time point. Accordingly, a block 62 that has a delta with aspecific vector ω_((p,q)) less than the upper bound r of delta values isenclosed, and the counts of all the vector combinations in the block 62are added to obtain a count sum similar to the vector ω_((p,q)).According to the number of occurrences of the target vector ω_((p,q)) inthe current accumulated data and total number of data in the range of rfrom the ω_((p,q)), this motion is performed on each position in theinformation matrix 60 to obtain all information required for calculatingthe approximate entropy or the sample entropy. Multiple groups of datasequences under multiple scales are considered, and the operation isexecuted to calculate the MSE index.

Besides the method for recording the metadata by using amulti-dimensional sparse matrix, an embodiment of the disclosure furtherprovides another method for recording the metadata by using a tree datastructure.

In particular, for example, as for a data sequence under each scale ofeach window, metadata corresponding to one window is recorded in a treedata structure, and then metadata corresponding to other windows isadded in sequence to this tree data structure, so that the tree datastructure include metadata including data characteristics of all datasegments up to a current window.

For example, FIG. 7( a), FIG. 7( b), and FIG. 7( c) show examples ofrecording metadata by using a tree data structure and calculating aphysiological index according to the metadata according to an embodimentof the disclosure. In this embodiment, a sample point in a data segmentX=(3, 10, 19, 23, 30, 37, 45, 59, 62, 70, 80, 89, 95, 98) is recorded ina binary tree data structure (as shown in FIG. 7( a)) or in a 1D treedata structure (as shown in FIG. 7( b)). When a physiological index iscalculated, an upper bound of delta values defined in the MSE is firstset, and then as for a specific sample point in the tree data structure,a range in the tree data structure that has a delta with the samplepoint less than the upper bound of delta values is searched, andfinally, a count sum of all vector combinations in the range iscalculated to serve as information required for calculating aphysiological index of approximate entropy/sample entropy. For example,in FIG. 7( c), the upper bound of delta values is set to be 30, as forthe sample point x=45, a range (that is, x=15-75) in the tree datastructure in FIG. 7( b) that has a delta with the sample point less thanthe upper bound of delta values is searched, and finally, a count sum ofall vector combinations in the range is calculated to serve asinformation required for calculating a physiological index ofapproximate entropy/sample entropy.

According to some embodiments, FIG. 8 is a function block diagram of asystem for calculating a physiological index for executing the methodsin FIG. 1 to FIG. 7 according to some embodiment.

A system for calculating a physiological index 800 includes a computersystem 810. The computer system 810 includes a processor 814 that isdirectly electrically connected to at least one storage medium 812. Inorder to enable a computer to execute calculation and analysis of aphysiological index of a detected physiological signal, like a signalanalyzer, a processor 814 is configured to execute or suspend a computerprogram code complied in the at least one storage medium 812.

In some embodiments, the processor 814 is a central processing unit(CPU), a multi-processor, a distributed processing system and/or asuitable processing unit. In at least one embodiment, the processor 814may obtain a physiological signal such as an ECG signal, a predeterminedstandard template and/or other information from the at least one storagemedium 812.

In some embodiments, the at least one storage medium 812 is anelectronic, magnetic, optical, electromagnetic, infrared, and/orsemiconductor system (instrument or device). For example, the at leastone storage medium 812 includes a semiconductor or solid-state memory, amagnetic tape, a portable computer disk, a random access memory (RAM), aread-only memory (ROM), a hard disk and/or an optical disk. In someembodiments that an optical disk is used, the at least one storagemedium 812 includes a compact disc read-only memory (CD-ROM), a compactdisc rewritable (CD-RW) and/or a digital video disk (DVD).

Additionally, the computer system 810 includes an input/output interface816 and a display 818. The input/output interface 816 and the processor814 are directly connected. In order to execute the methods described inFIG. 1 to FIG. 7, an operator or a health care professional may beallowed to operate the computer system 810. The operating conditions ofthe methods described in FIG. 1 to FIG. 7 can be shown in the display818 through a graphical user interface (GUI). The input/output interface816 and the display 818 allow an operator to operate the computer system810 in the manner of man-machine interaction.

In an embodiment, the computer system 810 may also include a networkinterface 822 that is directly connected to the processor 814. Thenetwork interface 822 allows the computer system 810 to communicate withone or more computer systems connected to a network 830. The networkinterface 822 includes a wireless network interface such as BLUETOOTH,wireless fidelity (WIFI), worldwide interoperability for microwaveaccess (WiMAX), general packet radio service (GPRS), and wide band codedivision multiple access (WCDMA); and a wired network interface such asETHERNET, universal sequence bus (USB) or IEEE-1394. In someembodiments, the methods in FIG. 1 to FIG. 7 may be executed in two ormore computer systems 810 in FIG. 8, for example, a physiological signalsuch as an ECG signal, a predetermined standard template and/or otherinformation can be exchanged between different computer systems throughthe network 830.

In at least one embodiment, the system for calculating a physiologicalindex 800 further includes a converter 840. The converter 840 is usedfor observing a detected organism individual/organ and convertingmovement of the organism individual/organ into a representative signal.In an embodiment of analyzing an ECG signal, the converter 840 is usedfor observing a detected heart and converting the movement of heartmuscle into an ECG signal.

The computer system 810 further has a transmission interface 824 that isdirectly connected to the converter 840 and the processor 814. Thetransmission interface 824 can bridge the converter 840 and theprocessor 814, and can output the obtained periodic signal in a formatof, for example, a discrete time signal. For example, if the converter840 obtains an ECG signal, the transmission interface 824 receives theECG signal from the converter 840, and outputs the ECG signal in theformat of an ECG data array to the processor 814. In some embodiments,the converter 840 converts one of the following phenomena of organismindividual into an electronic signal: heartbeat, respiration, ECG, brainwaves, oxygen saturation, and other physiological signals.

In order to verify that the method for calculating a physiological indexof the disclosure is superior to the prior art, in the disclosure, threedifferent calculation methods are used to evaluate the time forcalculating MSE, which includes an original method that MSE does notperform any data structure processing on data, the method of thedisclosure that metadata is stored as an orderly data structure and MSEis directly calculated in a manner of structured batch processing, andthe method of the disclosure of structured online calculation of MSE. Inthe method of online calculation of MSE, all time (including time forcalculating and updating metadata in each time segment entered and timefor calculating MSE) consumed in the whole calculation process and timefor an operator to actually wait for the system to operate complete MSE(including calculation and updating of metadata and calculation of MSEfor one time) are additionally evaluated and respectively represented asa structured online calculation method (for all) and a structure onlinecalculation method (for reaction time). The physiological data sequenceused in this embodiment is 24-h ECG data, which is RRI sequential dataobtained through automatic R wave characteristic point detection andectopic wave filtering and is manually corrected by a professional. Inthe following embodiments, in setting the window length, thephysiological data that gradually enters the system is divided in themanner of fixed data quantity, and the size of data of each window (timesegment) is set to be 10,000 batches.

FIG. 9 is a time analysis chart of calculating MSE when a scale is setto be 1 by using four evaluation methods according to an embodiment ofthe disclosure. It can be clearly seen from FIG. 9 that, the originalbrute force method without considering the data structure andcomputation efficiency is much poor than other evaluation methods in theperformance of time efficiency, especially when the batches of dataexceeds 30,000, the time required by the original brute force method isa hundredfold higher than the time required for other three evaluationmethods. It can also be verified from FIG. 9 that, the calculationcomplexity of the original brute force method is increased with theincrease in the number of batches of data by an exponential multiple.

FIG. 10 shows comparison of calculating MSE by using a structured methodaccording to an embodiment of the disclosure according to theexperimental setting in FIG. 9. It can be found in FIG. 9 that, when thedata quantity is less than 40,000, the structured batch calculationmethod is slightly more efficient than the structured online calculationmethod in terms of total calculation time, but when the number ofbatches of data is greater than 40,000, the structured onlinecalculation (all) method is superior to the brute force methods in termsof computation efficiency; as compared with the structured onlinecalculation (reaction time) method, the calculation time consumed by thestructured online calculation method is extremely short, and when thenumber of batches of data is 120,000, merely 1.5 seconds are consumed,which is about a half of that of the structured online calculationmethod (for all) and about one third of that of the structured batchcalculation method; and moreover, 1.5 seconds are the time that aclinical medical worker actually waits when operating and calculatingthe physiological index.

FIG. 11 is an analysis chart of total time consumed in calculating MSEwhen a scale is set to be 1 to 20 by using four evaluation methodsaccording to an embodiment of the disclosure. Similar to the trend shownin FIG. 9, the calculation time of the brute force method is much longerthan the time for calculating MES after the data is structured, and FIG.11 can also explain why MSE has effects in research reports but fails tobe popular. Taking the number of batches of data being 120,000 as anexample, the original calculation method needs a calculation time ofmore than 5,000 seconds, thus leaving a large space for improvement asfor real-time requirements in clinical evaluation applications.

FIG. 12 shows analysis of time for calculating MSE by using a datastructured method in FIG. 11. The difference between the results in FIG.9 and FIG. 10 lies in that, after the scales of 1 to 20 are considered,the total calculation time required by the structured online calculationmethod (for all) is higher than that of the brute force method, and thereason lies in that when the scale is greater than 3, the number ofbatches of data is greatly reduced after the physiological data issubjected to a coarse-graining procedure, and the processing efficiencyof directly using the structured batch calculation method is higher thanthat of using the online method, because additional time for updatingmetadata is required in processing data of each window by using theonline method, resulting in that the total calculation time is slightlyhigher than the time of the batch calculation method. As for the actualwaiting time of clinical medical worker, the calculation time of thestructured online calculation method (for reaction time) is similar tothe result shown in FIG. 10, the structured online calculation method(for reaction time) is more efficient than the structured batchcalculation method and the structured online calculation method (forall), and the calculation time is also merely a half of that requiredfor the structured online calculation method (for all) and about onethird of that required for the structured batch calculation method. Thefeasibility and computation efficiency of the method of the disclosureis also proved in the embodiment, through the online data processtechnology of sequential data learning, a long-term physiological indexis provided.

It should be noted that, in an embodiment, besides the matrix structureand the tree structure are used to calculate and update the metadata,statistics of data probability distribution is further used as themetadata.

In particular, the probability distribution adopted in an embodiment ofthe present application is normal distribution, and the correspondingstatistics is a mean value and a standard deviation. FIG. 13( a) andFIG. 13( b) show examples of recording metadata by using datadistribution statistics and calculating a physiological index accordingto the metadata according to an embodiment of the disclosure. When thedimension m=2, in this embodiment, all two-dimensional sample points arearranged and organized according to the value, and a first dimensionvalue of a sample point is fixed at a value, a histogram plotted byusing a second dimension value at the sample point is shown in FIG. 13(a). A curve 121 is a function graph of normal distribution, and it canbe seen from FIG. 13( a) that, second dimension information generated bythe normal distribution adopted in this embodiment when the firstdimension is fixed is close to the distribution properties of theoriginal data.

On the other hand, when the dimension m=3, the first dimension and thesecond dimension value of all three-dimensional sample points are fixedat a two dimension combination, a histogram of a third dimensioninformation and corresponding normal distribution curve are shown inFIG. 13( b). Similarly, the extent that the normal distribution is closeto the data characteristics can be seen from a curve 132 in FIG. 13( b).

After each window uses the distribution statistics as metadata, metadataupdating may be performed with the distribution statistics of thesubsequent windows, and an updating formula adopted in an embodiment isas follows:

$\begin{matrix}{{{\overset{\sim}{\mu}}_{t + 1} = {\left( {{{\overset{\sim}{N}}_{t} \cdot {\overset{\sim}{\mu}}_{t}} + {N_{t + 1} \cdot \mu_{t + 1}}} \right)/\left( {{\overset{\sim}{N}}_{t} + N_{t + 1}} \right)}};} \\{{{\overset{\sim}{\sigma}}_{t + 1} = \left\lbrack \frac{\begin{matrix}{{{\overset{\sim}{\sigma}}_{t}^{2} \cdot \left( {{\overset{\sim}{N}}_{t} - 1} \right)} + {\sigma_{t + 1}^{2} \cdot \left( {N_{t + 1} - 1} \right)} + {{\overset{\sim}{N}}_{t} \cdot \left( {{\overset{\sim}{\mu}}_{t} - {\overset{\sim}{\mu}}_{t + 1}} \right)^{2}} +} \\{N_{t + 1} \cdot \left( {\mu_{t + 1} - {\overset{\sim}{\mu}}_{t + 1}} \right)^{2}}\end{matrix}}{{\overset{\sim}{N}}_{t} + N_{t + 1} - 1} \right\rbrack^{1/2}};} \\{{\overset{\sim}{N}}_{t + 1} = {{\overset{\sim}{N}}_{t} + N_{t + 1}}}\end{matrix}$

In the formula, Ñ_(t), {tilde over (μ)}_(t), {tilde over (σ)}_(t)independently represent a number of samples and distribution statistics(including a mean value {tilde over (μ)}_(t) and a standard deviation{tilde over (6)}_(t)) when being updated to a t^(th) window; N_(t+1),μ_(t+1), σ_(t+1) represent a number of samples and distributionstatistics calculated by a (t+1)^(th) window; and Ñ_(t+1), {tilde over(μ)}_(t+1), {tilde over (σ)}_(t+1) are a number of samples anddistribution statistics when accumulated to the (t+1)^(th) window afterthe two groups of metadata are updated. The finally recorded metadataand the final metadata obtained by using a sequential data learningmethod are completely identical with the metadata calculated by usingthe batch processing method.

After obtaining the metadata, in this embodiment, the distributionstatistics may be standardized, in which a measure of area occupied bythe standardized distribution function in each interval/region ismultiplied by a number of samples having the first dimension equal to apredetermined value, a number of occurrences of each sample point (thatis, vector combination) required for statistics when calculating MSE canbe estimated.

FIG. 14( a) and FIG. 14( b) are total time analysis charts ofcalculating MSE when a scale is set to be 1 to 20 by using fiveevaluation methods according to an embodiment of the disclosure. Inorder to verify that the method for calculating a physiological index ofthe disclosure is superior to the prior art, in this embodiment, theoriginal calculation method, the structured batch calculation method,the structured online calculation method merely considering thestructured online (for reaction time) of the final reaction time, thebatch estimation method of the batch calculation distributionstatistics, the online estimation (for reaction time) graduallycalculating the distribution statistics and merely considering the finalreaction time are compared.

It can be seen from FIG. 14( a), the calculation time consumed by thebrute force method is in exponential growth, and is much higher thanthat of the four methods that uses the metadata for calculation. In themethods using the metadata, the method for calculating a physiologicalindex by using the distribution statistics and the matrix structure torecord the metadata is most efficient. When the number of batches ofdata is 60,000, the original calculation method needs to consume about1,300 seconds, but it can be seen from details in FIG. 14( b) that, thebatch calculation method using the distribution statistics merely needsto consume about 110 second, and the online estimation (for reactiontime) using online estimation and merely considering the final reactiontime merely needs 85 seconds, which is less than one tenth of thatrequired by the original method. As for the calculation method using thematrix structure, the structured batch calculation method merely needsabout 22 seconds, the time required for the structured online (forreaction time) is further reduced to 12 seconds, as compared with thebrute force method, the efficiency of the two methods is improved by 50times to 100 times. No matter the distribution statistics estimation orthe matrix structure method is used to calculate MSE, adopting agradually learning method can provide a physiological index, as comparedwith the batch method.

The disclosure may adopt online or batch processing.

An embodiment of the disclosure provides a computer readable recordingmedium with a stored program, which can complete the method when theprogram is loaded on a computer and is executed.

Based on aforesaid method and system, the application is able to processphysiological data that is continuously input into the system. Theapplication analyzes and processes the streaming of physiological dataevery preset time length or data amount instead of processing the wholephysiological data after the physiological data is input into thesystem, and therefore the physiological data can be monitored andevaluated in real time.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of thedisclosed embodiments without departing from the scope or spirit of thedisclosure. In view of the foregoing, it is intended that the disclosurecover modifications and variations of this disclosure provided they fallwithin the scope of the following claims and their equivalents.

1. A method for calculating a physiological index, applicable to anelectronic device, the method comprising: dividing a physiological datasequence into a plurality of windows, wherein each window comprises adata segment of the physiological data sequence; analyzing the datasegment in each window to obtain metadata that represents datacharacteristics of the data segment; updating the metadata comprisingthe data characteristics of all data segments in the windows up to aprevious window by using the metadata corresponding to one of thewindows to obtain the metadata comprising the data characteristics ofall data segments in the windows up to a current window; and calculatinga physiological index by using the updated metadata.
 2. The method forcalculating a physiological index according to claim 1, wherein the stepof dividing the physiological data sequence into the windows comprises:defining a size of the windows according to a fixed duration or a datalength; and dividing the physiological data sequence into a plurality ofdata segments that are not overlapped with each other according to thesize of the windows.
 3. The method for calculating a physiological indexaccording to claim 1, wherein the step of analyzing the data segment ineach window to obtain the metadata that represents the datacharacteristics of the data segment comprises: using a plurality ofscales to perform a coarse-graining procedure on the data segment ineach window to obtain a data sequence under the scales and using thedata sequence as metadata that represents the characteristics of thedata segment.
 4. The method for calculating a physiological indexaccording to claim 3, wherein the step of using the plurality of scalesto perform the coarse-graining procedure on the data segment in eachwindow to obtain the data sequence under the scales comprises: whenusing one of the scales to perform the coarse-graining procedure on thedata segment, selecting a plurality of batches of data in the datasegment in sequence in a cell of the scales, and calculating an averageof the selected data to use the average as a batch of data in the datasegment under the scale.
 5. The method for calculating a physiologicalindex according to claim 3, wherein the step of updating the metadatacomprising the data characteristics of all data segments in the windowsup to the previous window by using the metadata corresponding to one ofthe windows to obtain the metadata comprising the data characteristicsof all data segments in the windows up to the current window comprises:as for the data sequence of each window under each scale, recordingmetadata corresponding to one of the windows by using amulti-dimensional sparse matrix; and cumulating metadata correspondingto other windows in sequence to the multi-dimensional sparse matrix, sothat the multi-dimensional sparse matrix comprises the metadatacomprising the data characteristics of all the data segments up to thecurrent window.
 6. The method for calculating a physiological indexaccording to claim 5, wherein as for the data sequence of each windowunder each scale, the step of recording the metadata corresponding toone of the windows by using the multi-dimensional sparse matrixcomprises: recording a first count of each first vector combination in aplurality of first vector combinations corresponding to the metadata inthe multi-dimensional sparse matrix.
 7. The method for calculating aphysiological index according to claim 6, wherein the step of cumulatingthe metadata corresponding to the other windows in sequence to themulti-dimensional sparse matrix, so that the multi-dimensional sparsematrix comprises the metadata comprising the data characteristics of allthe data segments up to the current window comprises: cumulating asecond count of each second vector combination in a plurality of secondvector combinations of the metadata corresponding to the other windowsin sequence to the counts of the first vector combinations recorded inthe multi-dimensional sparse matrix.
 8. The method for calculating aphysiological index according to claim 6, wherein the step ofcalculating the physiological index by using the updated metadatacomprises: setting an upper bound of delta values defined in multi-scaleentropy (MSE); as for a specific vector combination in themulti-dimensional sparse matrix, enclosing a block in themulti-dimensional sparse matrix that has a delta with the specificvector combination less than the upper bound of delta values; andcalculating a count sum of all vector combinations in the block to serveas information required for calculating the physiological index.
 9. Themethod for calculating a physiological index according to claim 3,wherein the step of updating the metadata comprising the datacharacteristics of all data segments in the windows up to the previouswindow by using the metadata corresponding to one of the windows toobtain the metadata comprising the data characteristics of all datasegments in the windows up to the current window comprises: as for thedata sequence under each scale of each window, recording metadatacorresponding to one of the windows by using a tree data structure; andadding metadata corresponding to other windows in sequence to the treedata structure, so that the tree data structure comprises the metadatacomprising data characteristics of all data segments in the windows upto the current window.
 10. The method for calculating a physiologicalindex according to claim 9 wherein the step of calculating thephysiological index by using the updated metadata comprises: setting anupper bound of delta values defined in MSE; as for a specific samplepoint in the tree data structure, searching for a range in the tree datastructure that has a delta with the specific sample point less than theupper bound of delta values; and calculating a count sum of all samplepoints in the range to serve as the physiological index.
 11. The methodfor calculating a physiological index according to claim 9, wherein thetree data structure comprises a binary tree data structure and a 1D treedata structure.
 12. The method for calculating a physiological indexaccording to claim 3 wherein the step of updating the metadatacomprising the data characteristics of all data segments in the windowsup to the previous window by using the metadata corresponding to one ofthe windows to obtain the metadata comprising the data characteristicsof all data segments in the windows up to the current window comprises:as for the data sequence under each scale of each window, calculating adistribution statistics corresponding to one of the windows by using amathematical statistics analysis method to serve as metadatacorresponding to the window; and updating the metadata by using adistribution statistics corresponding to another windows in sequence toobtain metadata that represents a distribution statistics up to thecurrent window.
 13. The method for calculating a physiological indexaccording to claim 12, wherein the data distribution is normaldistribution, and the parameters of distribution is a mean value and astandard deviation.
 14. The method for calculating a physiological indexaccording to claim 1, wherein the physiological data sequence comprisesa data sequence of features of an electrocardiogram (ECG), features ofelectroencephalogram, a breathing signal or a oxygen saturation signals.15. The method for calculating a physiological index according to claim14, wherein the features of an ECG comprise an R-R interval of adjacentheartbeats, a P-R interval in a single heartbeat, a QRS duration, an STsegment duration in the ECG measured from a temporal perspective, afirst delta of a P wave, an R wave, an S wave, and a T wave potentialchange between adjacent heartbeats measured from a spatial perspective,and a second delta or a similarity of a pattern difference betweenadjacent ECGs measured from a morphological perspective.
 16. The methodfor calculating a physiological index according to claim 1, wherein themetadata comprises statistical descriptions, data structurecharacteristics, trend information, or a data randomness measurement forrepresenting data characteristics.
 17. The method for calculating aphysiological index according to claim 16, wherein the statisticaldescriptions comprise a mean value, a standard deviation, a mode, amedian, a coefficient of skewness, a coefficient of kurtosis, orparameters of probability distribution, the data structurecharacteristics comprises grouping or counting values of data histogram,the trend information comprises a regression coefficient or a polynomialcoefficient, and the data randomness comprises entropy or a temporalasymmetric index.
 18. A system for calculating a physiological index,comprising: a converter, detecting a physiological data sequence; and acomputer system, comprising: a transmission interface, connected to theconverter, receiving the physiological data sequence; at least onestorage medium, storing the physiological data sequence; and aprocessor, coupled to the transmission interface and the at least onestorage medium, dividing the physiological data sequence into aplurality of windows, analyzing a data segment of the physiological datasequence in each window to obtain metadata that represents datacharacteristics of the data segment, updating metadata including thedata characteristics of all data segments in the windows up to aprevious window by using the metadata corresponding to one of thewindows to obtain the metadata including data characteristics of alldata segments in the windows up to a current window; and calculating aphysiological index by using the updated metadata.
 19. The system forcalculating a physiological index according to claim 18, furthercomprising: a display, connected to the processor, displaying agraphical user interface for operating the computer system; and aninput/output interface, connected to the processor, receiving anoperation of a user on the computer system.
 20. The system forcalculating a physiological index according to claim 18, furthercomprising: a network interface, connected to the processor,communicating with other computer systems through a network.
 21. Acomputer readable recording medium for storing a program, capable ofimplementing the method according to any one of claims 1 to 17 after theprogram is loaded on a computer and is executed.